Explainability: Actionable Information Extraction

Actionable information extraction has recently become a very attractive research area. Information extraction has been around for a while, but usually the actions that can be triggered or supported by the extracted information have been seldom considered. Currently, a plethora of algorithms is used to create models that provide information extraction abilities from different types of data with different types of applications. In this paper we propose to use a distillation method based on decision-trees that transfers knowledge from black-box models to more interpretable models to understand the decision patterns in different applications. Prediction results on a credit score problem show that it is possible to use white-box methods that work on black-box results to show the potential interpretation of the decision patterns.